A Novel Intelligent Power Control Technique for a Type-3 Wind Energy Conversion System with LVRT Capability and Improved Dynamic Performance
Bibliographic record
Abstract
This paper represents a novel neuro-fuzzy (NF) based intelligent power control (IPC) technique for a Type-3 wind energy conversion system (WECS), which can achieve low voltage ride through (LVRT) capability by managing grid side disturbance of WECS. The proposed IPC technique considers the errors between command and actual values of real and reactive powers of the stator as inputs and processes these inputs through two NF networks to generate d-q axis switching signals for the switches of rotor side converters (RSCs). Additionally, a hybrid training method is developed to train the NF system parameters. The performance of the proposed control technique is tested in simulation under different grid disturbance and wind speed variations. Furthermore, a numerical comparative study of performances is conducted between the proposed and the classical proportional-integral control technique at different operating conditions. A laboratory prototype of Type-3 WECS is also built to test the performance of the proposed IPC technique in real-time using the DSP controller board DS-1104. Both the simulation and experimental results prove the LVRT capability of the proposed scheme as well as its satisfactory dynamic response of the WECS coping with variations in wind speed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".